Position/Title: Ph.D. Candidate | Livestock Data Technician
Phone: (519) 824-4120 ext. 53786
Office: ANNU 016B
Lucas obtained his Bachelor’s in Biotechnology (2015) and a Master’s in Biosciences (2017) from the Federal University of Bahia, Brazil.
As part of numerous research projects during his undergraduate studies, he acquired experience in genetic polymorphisms, molecular biology, fermentative processes, bioprospection of microorganisms, and the production and purification of industrial enzymes. He was an exchange student at the University of Toronto (2013), where he improved his skills in molecular biology and enzymology while studying the biological degradation of pine tree bark by different fungi species for bioethanol production.
As a master’s student, he decided to understand the role of different bacteria from the rumen in the degradation of plant cell-wall through bioinformatics analysis and to target species with the potential to produce enzymes of industrial interest. His passion for research and new challenges brought him to the Centre for Genetic Improvement of Livestock (CGIL) at the University of Guelph in 2018 to pursue a Ph.D. in Animal Breeding and Genetics, where he seeks to understand the impact of cutting-edge technologies on breeding strategies for optimum sustainable genetic progress in Canadian dairy cattle, under the advisement of Dr. Flavio Schenkel and Dr. Christine Baes.
In addition to his Ph.D. studies, he currently works part-time at the OMAFRA Elora/Ponsonby Livestock Research Centres as a Livestock Data Technician.
Current Research Projects
- Conformation traits of Holstein cows and their association with the Pro$ selection index. There are currently more than 20 conformation traits being genetically evaluated for Canada Holsteins. Understanding the contribution of each of these traits to a monetary index, such as the national selection index Pro$, would help producers make culling and mating decisions to achieve more profitable herds. This study uses multiple linear regression and principal component analysis to assess the association between conformation traits and Pro$. A video of the oral presentation at the 2020 American Dairy Science Association Meeting with some of the results from this research is publicly available here. This work is being done under the advisement of Dr. Flavio Schenkel and Dr. Christine Baes, in collaboration with Dr. Gerson Oliveira.
- Classification of breeding code protocol descriptions used with Canadian Holsteins. Dairy farmers are constantly investing in ways to ensure cows become pregnant in an optimal and timely manner. Timed artificial insemination (TAI) has proven to be a successful management tool in dairy cattle, but it is known to mask an animal’s true fertility performance, reducing the accuracy of genetic evaluations for fertility traits. Splitting fertility traits by the management technique involved in the breeding can be a viable technique to address the bias. Nonetheless, there is a lack of specificity and uniformity in the recording of breeding code protocol descriptions (BC) by dairy farmers. Therefore, this project proposes the development of a supervised machine learning model to classify BC into four classes: 1) heat detection, 2) TAI, 3) hormone use, or 4) other (unidentifiable) protocols, opening the way for unbiased genetic evaluation of animals according to their true fertility performance. This work is being done under the advisement of Dr. Flavio Schenkel and Dr. Christine Baes, in collaboration with Colin Lynch, Dr. Gerson Oliveira, and Dr. Dan Tulpan.
- Intelligent algorithms for the prediction of dairy bull fertility. Bovine fertility is a multifactorial process that relies on the quality of semen, female fertility, proper management of herds, and accurate timing of artificial insemination. Much attention has been given to the later factors, and, even though a clear impact on reproduction has been seen and attributed to poor semen fertility, not much progress has been achieved in this regard. Due to the complexity of the variables involved in predicting bull fertility, machine learning (ML) and deep learning (DL) algorithms appear as promising methods for predicting bull fertility. Therefore, this study uses ML and DL algorithms to accurately predict bull fertility from semen quality data along with cow fertility and insemination records from Canadian Holsteins. This work is being done under the advisement of Dr. Flavio Schenkel and Dr. Christine Baes, in collaboration with Dr. Dan Tulpan.
Featured Web-Applications Developed
- https://cgil.shinyapps.io/correlations - Interactive tool to facilitate visualization of genetic parameters of all currently genetically evaluated traits of Canadian Holsteins (correlation matrix, network plot, genetic trends, trait definitions, etc)
- https://alcantara.shinyapps.io/covid - Daily updates of COVID-19 notifications in Brazil with simple interactive graphs [Available in Portuguese only]
- Oliveira Junior, G.A., F.S. Schenkel, L. Alcantara, K. Houlahan, C. Lynch, and C.F. Baes. 2021. Estimated genetic parameters for all genetically evaluated traits in Canadian Holsteins. Journal of Dairy Science. https://doi.org/10.3168/jds.2021-20227.